Genetic association mapping and genome organization of maize Jianming Yu and Edward S Buckler Association mapping, a high-resolution method for mapping quantitative trait loci based on linkage disequilibrium, holds great promise for the dissection of complex genetic traits. The recent assembly and characterization of maize association mapping panels, development of improved statistical methods, and successful association of candidate genes have begun to realize the power of candidate-gene association mapping. Although the complexity of the maize genome poses several significant challenges to the application of association mapping, the ongoing genome sequencing project will ultimately allow for a thorough genome-wide examination of nucleotide polymorphism-trait association. Addresses Institute for Genomic Diversity and United States Department of Agriculture – Agricultural Research Service, and Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853, USA Corresponding author: Buckler, Edward S (esb33@cornell.edu) Current Opinion in Biotechnology 2006, 17:155–160 This review comes from a themed issue on Plant biotechnology Edited by Nam-Hai Chua and Scott V Tingey Available online 28th February 2006 0958-1669/$ – see front matter Published by Elsevier Ltd. DOI 10.1016/j.copbio.2006.02.003 Introduction Most traits of agricultural or evolutionary importance are controlled by multiple quantitative trait loci (i.e. complex traits). Genetic mapping and molecular characterization of these functional loci facilitates genome-aided breeding for crop improvements such as disease resistance, effi- ciency of fertilizer use, and drought tolerance. Two of the most commonly used tools for dissecting complex traits are linkage analysis and association mapping [1,2]. Link- age analysis exploits the shared inheritance of functional polymorphisms and adjacent markers within families or pedigrees of known ancestry. Linkage analysis in plants has been typically conducted with experimental popula- tions that are derived from a bi-parental cross. Although based on the same fundamental principles of genetic recombination as linkage analysis, association mapping examines this shared inheritance for a collection of indi- viduals often with unobserved ancestry. As the unob- served ancestry can extend thousands of generations, the shared inheritance will only persist for adjacent loci after these many generations of recombination. Essentially, association mapping exploits historical and evolutionary recombination at the population level [3,4]. By exploring deeper population genealogy rather than family pedigree, association mapping offers three advan- tages over linkage analysis: much higher mapping resolu- tion; greater allele number and broader reference population; and less research time in establishing an association [5,6](Figure 1). Linkage analysis and association mapping, however, are complimentary to each other in terms of providing prior knowledge, cross-validation, and statistical power [7  ]. Systematic comparisons of these two different approaches have been reviewed elsewhere both in general [8  ] and more specifically in maize [7  ]. Procedures for conduct- ing an association mapping study in plants have also been well documented [7  ,9]. Here, we will focus on recent advances in association mapping conducted in maize, and discuss maize genome structure and its implications for association mapping. Linkage disequilibrium The comparatively high-resolution provided by associa- tion mapping is dependent upon the structure of linkage disequilibrium (LD) across the genome. Linkage dise- quilibrium (LD) refers to the non-random association of alleles between genetic loci. Many genetic and non- genetic factors, including recombination, drift, selection, mating pattern, and admixture (i.e. a population of sub- groups with different allele frequencies), affect the struc- ture of LD [6,10]. The key to association mapping is the LD between functional loci and markers that are physi- cally linked. The decay of LD over physical distance in a population determines the density of marker coverage needed to perform an association analysis. For example, if LD decays rapidly, then a higher marker density is required to capture markers located close enough to functional sites. Studies have shown that LD levels vary both within and between species [6]. For example, LD extends less than 1000 bp [11] for maize landraces and roughly 2000 bp for diverse maize inbred lines [4], but can be as high as 100 kb for commercial elite inbred lines [12]. LD decay can also vary considerably from locus to locus. For exam- ple, significant LD was observed up to 4 kb for the Y1 locus (encoding phytonene synthase), but was seen at only 1 kb for PSY2 (a putative phytonene synthase) in the same maize population [13  ]. A more recent study showed that LD extends over 800 kb around Y1 [14  ], www.sciencedirect.com Current Opinion in Biotechnology 2006, 17:155–160